Discriminating hand gesture motor imagery tasks using cortical current density estimation

Clinical applications of neurofeedback based on sensorimotor rhythm: a systematic review and meta-analysis

Background

Among the brain-machine interfaces, neurofeedback is a non-invasive technique that uses sensorimotor rhythm (SMR) as a clinical intervention protocol. This study aimed to investigate the clinical applications of SMR neurofeedback to understand its clinical effectiveness in different pathologies or symptoms.

Methods

A systematic review study with meta-analysis of the clinical applications of EEG-based SMR neurofeedback performed using pre-selected publication databases. A qualitative analysis of these studies was performed using the Consensus tool on the Reporting and Experimental Design of Neurofeedback studies (CRED-nf). The Meta-analysis of clinical efficacy was carried out using Review Manager software, version 5.4.1 (RevMan 5; Cochrane Collaboration, Oxford, UK).

Results

The qualitative analysis includes 44 studies, of which only 27 studies had some kind of control condition, five studies were double-blinded, and only three reported a blind follow-up throughout the intervention. The meta-analysis included a total sample of 203 individuals between stroke and fibromyalgia. Studies on multiple sclerosis, insomnia, quadriplegia, paraplegia, and mild cognitive impairment were excluded due to the absence of a control group or results based only on post-intervention scales. Statistical analysis indicated that stroke patients did not benefit from neurofeedback interventions when compared to other therapies (Std. mean. dif. 0.31, 95% CI 0.03-0.60, p = 0.03), and there was no significant heterogeneity among stroke studies, classified as moderate I2 = 46% p-value = 0.06. Patients diagnosed with fibromyalgia showed, by means of quantitative analysis, a better benefit for the group that used neurofeedback (Std. mean. dif. -0.73, 95% CI -1.22 to -0.24, p = 0.001). Thus, on performing the pooled analysis between conditions, no significant differences were observed between the neurofeedback intervention and standard therapy (0.05, CI 95%, -0.20 to -0.30, p = 0.69), with the presence of substantial heterogeneity I2 = 92.2%, p-value < 0.001.

Conclusion

We conclude that although neurofeedback based on electrophysiological patterns of SMR contemplates the interest of numerous researchers and the existence of research that presents promising results, it is currently not possible to point out the clinical benefits of the technique as a form of clinical intervention. Therefore, it is necessary to develop more robust studies with a greater sample of a more rigorous methodology to understand the benefits that the technique can provide to the population.

Review of brain encoding and decoding mechanisms for EEG-based brain-computer interface

A brain-computer interface (BCI) can connect humans and machines directly and has achieved successful applications in the past few decades. Many new BCI paradigms and algorithms have been developed in recent years. Therefore, it is necessary to review new progress in BCIs. This paper summarizes progress for EEG-based BCIs from the perspective of encoding paradigms and decoding algorithms, which are two key elements of BCI systems. Encoding paradigms are grouped by their underlying neural meachanisms, namely sensory- and motor-related, vision-related, cognition-related and hybrid paradigms. Decoding algorithms are reviewed in four categories, namely decomposition algorithms, Riemannian geometry, deep learning and transfer learning. This review will provide a comprehensive overview of both modern primary paradigms and algorithms, making it helpful for those who are developing BCI systems.

Empirical Comparison of Distributed Source Localization Methods for Single-Trial Detection of Movement Preparation

The development of technologies for the treatment of movement disorders, like stroke, is still of particular interest in brain-computer interface (BCI) research. In this context, source localization methods (SLMs), that reconstruct the cerebral origin of brain activity measured outside the head, e.g., via electroencephalography (EEG), can add a valuable insight into the current state and progress of the treatment. However, in BCIs SLMs were often solely considered as advanced signal processing methods that are compared against other methods based on the classification performance alone. Though, this approach does not guarantee physiological meaningful results. We present an empirical comparison of three established distributed SLMs with the aim to use one for single-trial movement prediction. The SLMs wMNE, sLORETA, and dSPM were applied on data acquired from eight subjects performing voluntary arm movements. Besides the classification performance as quality measure, a distance metric was used to asses the physiological plausibility of the methods. For the distance metric, which is usually measured to the source position of maximum activity, we further propose a variant based on clusters that is better suited for the single-trial case in which several sources are likely and the actual maximum is unknown. The two metrics showed different results. The classification performance revealed no significant differences across subjects, indicating that all three methods are equally well-suited for single-trial movement prediction. On the other hand, we obtained significant differences in the distance measure, favoring wMNE even after correcting the distance with the number of reconstructed clusters. Further, distance results were inconsistent with the traditional method using the maximum, indicating that for wMNE the point of maximum source activity often did not coincide with the nearest activation cluster. In summary, the presented comparison might help users to select an appropriate SLM and to understand the implications of the selection. The proposed methodology pays attention to the particular properties of distributed SLMs and can serve as a framework for further comparisons.

Decoding complex imagery hand gestures

Brain computer interfaces (BCIs) offer individuals suffering from major disabilities an alternative method to interact with their environment. Sensorimotor rhythm (SMRs) based BCIs can successfully perform control tasks; however, the traditional SMR paradigms intuitively disconnect the control and real task, making them non-ideal for complex control scenarios. In this study we design a new, intuitively connected motor imagery (MI) paradigm using hierarchical common spatial patterns (HCSP) and context information to effectively predict intended hand grasps from electroencephalogram (EEG) data. Experiments with 5 participants yielded an aggregate classification accuracy-intended grasp prediction probability-of 64.5% for 8 different hand gestures, more than 5 times the chance level.

EEG Source Imaging Enhances the Decoding of Complex Right-Hand Motor Imagery Tasks

GOAL

Sensorimotor-based brain-computer interfaces (BCIs) have achieved successful control of real and virtual devices in up to three dimensions; however, the traditional sensor-based paradigm limits the intuitive use of these systems. Many control signals for state-of-the-art BCIs involve imagining the movement of body parts that have little to do with the output command, revealing a cognitive disconnection between the user's intent and the action of the end effector. Therefore, there is a need to develop techniques that can identify with high spatial resolution the self-modulated neural activity reflective of the actions of a helpful output device.

METHODS

We extend previous EEG source imaging (ESI) work to decoding natural hand/wrist manipulations by applying a novel technique to classifying four complex motor imaginations of the right hand: flexion, extension, supination, and pronation.

RESULTS

We report an increase of up to 18.6% for individual task classification and 12.7% for overall classification using the proposed ESI approach over the traditional sensor-based method.

CONCLUSION

ESI is able to enhance BCI performance of decoding complex right-hand motor imagery tasks.

SIGNIFICANCE

This study may lead to the development of BCI systems with naturalistic and intuitive motor imaginations, thus facilitating broad use of noninvasive BCIs.

Noninvasive Brain-Computer Interfaces Based on Sensorimotor Rhythms

Brain-computer interfaces (BCIs) have been explored in the field of neuroengineering to investigate how the brain can use these systems to control external devices. We review the principles and approaches we have taken to develop a sensorimotor rhythm EEG based brain-computer interface (BCI). The methods include developing BCI systems incorporating the control of physical devices to increase user engagement, improving BCI systems by inversely mapping scalp-recorded EEG signals to the cortical source domain, integrating BCI with noninvasive neuromodulation strategies to improve learning, and incorporating mind-body awareness training to enhance BCI learning and performance. The challenges and merits of these strategies are discussed, together with recent findings. Our work indicates that the sensorimotor-rhythm-based noninvasive BCI has the potential to provide communication and control capabilities as an alternative to physiological motor pathways.